An accurate understanding of tornadogenesis has been an unachieved goal of the meteorological sciences, in spite of a great number of research and observations made over many decades. The ability to predict and detect tornadogenesis lacks far behind that of other meteorological phenomena, although tornados are among the most potentially dangerous weather events. Tornado data assimilation benefits from an appropriate dynamical model and observational input data. The dynamical model utilized in current applications is a full set of governing equations of motion, mass continuity, thermodynamics, and cloud-physics. The dynamical model has been tested by tornado simulations. Starting from the numerical simulation of a supercell storm, many simulations were successful in reproducing supercell and mesocyclones, but not tornadoes. Indeed, it has been concluded that tornadoes develop from only about 20% of mesocyclones, indicating that a full understanding that tornadogenesis is still needed. Recent advanced observations and successful computer simulations of tornadogenesis clearly suggested super high spatial resolution and the associated temporal resolution are required to solve a full set of governing equations of motion, mass continuity, thermodynamics and cloud-physics by computer. For example, in the first successful simulation of tornadogenesis for a few hours of evolution time, ARPS (Advanced Regional Prediction System, Version 4.5) was used with horizontal grid size of 70 m, not nested, and 45 levels of vertical grid, with 10-m spacing near the ground, with associated time increments on the time split integration scheme; Δt =0.03 s, 0.18 s; the former is for sound wave and the latter for others). The simulation took about 720 hours on the IBM Regatta computer of 16 nodes at Tokyo University.
It can take several days or weeks of computer execution time to simulate tornado evolution of a few hours by the supercomputers currently available for weather forecasting. Such computing capacity requirements prohibit direct application of current full simulation models for practical operational use to detect or predict tornadoes in real time. Recent advanced observations such as phased-array Doppler radar and mobile X-band radars have also revealed spatial and temporal details of similar high resolutions that are useful for understanding tornadogenesis and desirably would be properly reflected in data assimilation. However, again, the presently-available computing power is not sufficient for practical operational forecasting or detection of tornadoes with conventional numerical models. A method and system able to predict and/or detect a tornado and issue warnings during real time would be highly desirable. It is to such a method and system that the present disclosure is directed.